#include "roi_align.h"

#include <torch/autograd.h>
#include <torch/types.h>

namespace vision {
namespace ops {

at::Tensor roi_align(
    const at::Tensor& input, // Input feature map.
    const at::Tensor& rois, // List of ROIs to pool over.
    double spatial_scale, // The scale of the image features. ROIs will be
    // scaled to this.
    int64_t pooled_height, // The height of the pooled feature map.
    int64_t pooled_width, // The width of the pooled feature
    int64_t sampling_ratio, // The number of points to sample in each bin
    bool aligned) // The flag for pixel shift
// along each axis.
{
  static auto op = c10::Dispatcher::singleton()
                       .findSchemaOrThrow("torchvision::roi_align", "")
                       .typed<decltype(roi_align)>();
  return op.call(
      input,
      rois,
      spatial_scale,
      pooled_height,
      pooled_width,
      sampling_ratio,
      aligned);
}

at::Tensor _roi_align_backward(
    const at::Tensor& grad,
    const at::Tensor& rois,
    double spatial_scale,
    int64_t pooled_height,
    int64_t pooled_width,
    int64_t batch_size,
    int64_t channels,
    int64_t height,
    int64_t width,
    int64_t sampling_ratio,
    bool aligned) {
  static auto op =
      c10::Dispatcher::singleton()
          .findSchemaOrThrow("torchvision::_roi_align_backward", "")
          .typed<decltype(_roi_align_backward)>();
  return op.call(
      grad,
      rois,
      spatial_scale,
      pooled_height,
      pooled_width,
      batch_size,
      channels,
      height,
      width,
      sampling_ratio,
      aligned);
}

TORCH_LIBRARY_FRAGMENT(torchvision, m) {
  m.def(
      "roi_align(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio, bool aligned) -> Tensor");
  m.def(
      "_roi_align_backward(Tensor grad, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int batch_size, int channels, int height, int width, int sampling_ratio, bool aligned) -> Tensor");
}

namespace {

class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
 public:
  static torch::autograd::variable_list forward(
      torch::autograd::AutogradContext* ctx,
      const torch::autograd::Variable& input,
      const torch::autograd::Variable& rois,
      double spatial_scale,
      int64_t pooled_height,
      int64_t pooled_width,
      int64_t sampling_ratio,
      bool aligned) {
    ctx->saved_data["spatial_scale"] = spatial_scale;
    ctx->saved_data["pooled_height"] = pooled_height;
    ctx->saved_data["pooled_width"] = pooled_width;
    ctx->saved_data["sampling_ratio"] = sampling_ratio;
    ctx->saved_data["aligned"] = aligned;
    ctx->saved_data["input_shape"] = input.sizes();
    ctx->save_for_backward({rois});
    at::AutoNonVariableTypeMode g;
    auto result = roi_align(
        input,
        rois,
        spatial_scale,
        pooled_height,
        pooled_width,
        sampling_ratio,
        aligned);
    return {result};
  }

  static torch::autograd::variable_list backward(
      torch::autograd::AutogradContext* ctx,
      const torch::autograd::variable_list& grad_output) {
    // Use data saved in forward
    auto saved = ctx->get_saved_variables();
    auto rois = saved[0];
    auto input_shape = ctx->saved_data["input_shape"].toIntList();
    auto grad_in = _roi_align_backward(
        grad_output[0],
        rois,
        ctx->saved_data["spatial_scale"].toDouble(),
        ctx->saved_data["pooled_height"].toInt(),
        ctx->saved_data["pooled_width"].toInt(),
        input_shape[0],
        input_shape[1],
        input_shape[2],
        input_shape[3],
        ctx->saved_data["sampling_ratio"].toInt(),
        ctx->saved_data["aligned"].toBool());
    return {grad_in,
            torch::autograd::Variable(),
            torch::autograd::Variable(),
            torch::autograd::Variable(),
            torch::autograd::Variable(),
            torch::autograd::Variable(),
            torch::autograd::Variable()};
  }
};

// TODO: There should be an easier way to do this
class ROIAlignBackwardFunction
    : public torch::autograd::Function<ROIAlignBackwardFunction> {
 public:
  static torch::autograd::variable_list forward(
      torch::autograd::AutogradContext* ctx,
      const torch::autograd::Variable& grad,
      const torch::autograd::Variable& rois,
      double spatial_scale,
      int64_t pooled_height,
      int64_t pooled_width,
      int64_t batch_size,
      int64_t channels,
      int64_t height,
      int64_t width,
      int64_t sampling_ratio,
      bool aligned) {
    at::AutoNonVariableTypeMode g;
    auto result = _roi_align_backward(
        grad,
        rois,
        spatial_scale,
        pooled_height,
        pooled_width,
        batch_size,
        channels,
        height,
        width,
        sampling_ratio,
        aligned);
    return {result};
  }

  static torch::autograd::variable_list backward(
      torch::autograd::AutogradContext* ctx,
      const torch::autograd::variable_list& grad_output) {
    TORCH_CHECK(0, "double backwards on roi_align not supported");
  }
};

at::Tensor roi_align_autograd(
    const at::Tensor& input,
    const at::Tensor& rois,
    double spatial_scale,
    int64_t pooled_height,
    int64_t pooled_width,
    int64_t sampling_ratio,
    bool aligned) {
  return ROIAlignFunction::apply(
      input,
      rois,
      spatial_scale,
      pooled_height,
      pooled_width,
      sampling_ratio,
      aligned)[0];
}

at::Tensor roi_align_backward_autograd(
    const at::Tensor& grad,
    const at::Tensor& rois,
    double spatial_scale,
    int64_t pooled_height,
    int64_t pooled_width,
    int64_t batch_size,
    int64_t channels,
    int64_t height,
    int64_t width,
    int64_t sampling_ratio,
    bool aligned) {
  return ROIAlignBackwardFunction::apply(
      grad,
      rois,
      spatial_scale,
      pooled_height,
      pooled_width,
      batch_size,
      channels,
      height,
      width,
      sampling_ratio,
      aligned)[0];
}

} // namespace

TORCH_LIBRARY_IMPL(torchvision, Autograd, m) {
  m.impl("roi_align", roi_align_autograd);
  m.impl("_roi_align_backward", roi_align_backward_autograd);
}

} // namespace ops
} // namespace vision
